Table of Contents

• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

This dataset is available as a large.zip file (1.8 GB compressed; 3.5 GB uncompressed) available for Download (WARNING: LARGE FILE). Each folder represents a different experiment. In the folders are:

1) A series of txt files, each of which describes 1 independent sample of a 3D velocity vector field measured in an x-y plane in the turbulence tank. The data is 5-column tab-delimited data:
Column 1 is x-location of velocity measurement (in mm, and defined relative to origin at the tank’s geometric center)
Column 2 is y-location (in mm)
Column 3 is x-component of velocity vector (in m/s)
Column 4 and 5 are y- and z-components of velocity vector, respectively (in m/s)

2) A *.mat file, including the same data as above, but arranged into an LxMxN matrix, in which L is x-location, M is y-location, and N is number of independent samples.  Velocity components are called UU,VV, and WW.  The x- and y- values are called xvals and yvals.  This file is constructed from the *.txt files using the *.m file found in the root directory “view_and_organize_vectors_from_Darling.m”

3) Eight *.png figures that summarize the turbulent features of velocity fields.

4) A *.dat file giving some key statistics describing the turbulent features of velocity fields.  This file is constructed from the *.mat files using the *.m file found in the root directory “analyze_vectors_from_Darling.m”

The txt files are output from the DaVis 8 software program, using Stereoscopic PIV (particle image velocimetry) mode. This software computes the velocity vectors (given in the txt files) from digital video captured in a fluid flow The full DaVis 8 steroscopic PIV process is described in the documentation provided by LaVision GMBH. Key processing parameters are recorded in the file “notes.txt” in the root directory. 

All processing to this data is explained in the two commented *.m-files in the root directory.

Description from NSF award abstract:
Turbulence is ubiquitous and important in the upper mixed layer where phytoplankton reside. On scales relevant to individual cells and colonies, dissipating turbulence erodes molecular diffusive boundary layers around cells and affects probabilities of encounter critical for processes such as predator-prey interactions and aggregation. Superimposed on ambient fluid motion are local motions of cells due to tumbling and sinking. How these motions interact with ambient turbulence is not well understood, particularly for the morphologically diverse, non-motile diatoms that dominate primary productivity. Recent studies show that still-water sinking velocities of particles and turbulent diffusive motions of fluids cannot simply be added together to solve for particle motions; for negatively and nearly neutrally buoyant particles, high turbulence intensity produces several times faster sinking and rising velocities than seen in still water. Atmospheric studies of water droplets in clouds have focused on inertial mechanisms of acceleration, but the review in this proposal suggests that--in the parameter space defined by the particle characteristics of diatoms--shape may be much more important than inertia in determining plankton-turbulence interactions. The investigators propose to study effects of turbulence on settling trajectories and resultant spatial distributions of diatoms. Employing a novel volumetric particle imager, they will obtain 3D Lagrangian trajectories of individual cells and chains of selected strains of diatoms under controlled turbulence. By targeting species of different morphologies and mechanical properties as well as cultures at different growth stages, new insights will be gained on how physical, morphological and physiological properties affect sinking speeds and trajectories of non-motile phytoplankton.

Sinking is an important life strategy of phytoplankton, and its interaction with ambient flows holds significant implications for the residence time of phytoplankton in the mixed layer and ultimately for phytoplankton productivity in the photic zone and fluxes of elements to the deeper ocean. This study challenges common views on mechanisms that cause phytoplankton sinking in the ocean and offers a mechanistic dissection of departures in turbulent sinking from Stokes settling. By targeting species of varied morphologies and mechanical properties, as well as cultures at different growth stages, new insights will be gained into how physical, morphological and physiological properties interact to determine sinking speeds and trajectories of non-motile phytoplankton, particularly in and near the vortices that dominate turbulent flows. Results from this study will also advance understanding of underlying spatial distributions of phytoplankton in the poorly resolved decimeter to millimeter domain, with abundant implications for the foraging fields of grazers and formation of particle aggregates.